Low-power wireless sensor and actor networks (LP-WSAN) standards require low power and a simplified protocol. FIG. 4 illustrates a low-power wireless sensor and actor network 400 having a sensor 410 and an actor 420. The sensors are multifunctional devices that communicate untethered in short distances. The actors are resource-rich devices with higher processing and transmission capabilities, and collect and process sensor information and perform actions based on the information gathered.
The low power and simplified protocol requirements of LP-WSANs compromise performance, such as in packet error rate (PER). In the Bluetooth low energy (BT-LE) standard, for example, if there is one packet error, there are two additional packets transmitted—one packet informing that the previously sent packet was not received correctly, and another packet to repeat the operation. The result is an increased average energy per effectively communicated bit.
Many LP-WSANs are based on continuous phase modulation (CPM). CPM is a method for modulation of information where, in contrast to other phase modulation techniques in which the carrier phase abruptly resets to zero at the start of every symbol, the carrier phase is modulated continuously. In order for a CPM receiver to have a low PER, it requires perfect knowledge of modulation parameters. However, because of the laxity when constructing the receiver, the modulating parameters of peer transmitting devices are unknown, and moreover, vary between devices. This complicates construction of a coherent detector, which exploits knowledge of the carrier's phase and maximizes BER (bit error rate)/PER performance.
The optimal detection scheme for CPM to maximize performance is Maximum Likelihood Sequence Detection (MLSD). MLSD is a mathematical algorithm for optimally extracting useful information out of the CPM noisy received CPM signal. MLSD is only optimal if, and only if, the noise added to the received CPM signal is white and Gaussian. The complexity of implementing MLSD for CPM signals stems from its structure depending on the modulation index of the received signal. The modulation index determines the topology of the detection scheme, that is, the trellis structure, and can increase the number of states in the trellis exponentially. Moreover, estimation and equalization of the modulation index is nonlinear, making an MLSD implementation with variable modulation index capability not feasible.